Finally, Vec2Instance allows us to eliminate transpose convolution operations [1]} consisting of trainable parameters common in segmentation networks, reducing the total number of trainable parameters in the network. Since all instances are generated through a centroid vector, the centroid vector then becomes a bottleneck. With this bottleneck, the Vec2Instance decoder performs better than a transpose convolution decoder. This is demonstrated in Figure REF  by loss curves over the test dataset of a decoder with Vec2Instance (200,000 total trainable parameters) and two decoders with transpose convolutions (200,000 and 300,000 total trainable parameters).
